SVM‐based segmentation and classification of remotely sensed data

Lizarazo, Ivan

doi:10.1080/01431160802326081

Cited by 42 publications

(23 citation statements)

References 10 publications

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“…Indeed, for remotely sensed data, it has been shown that SVM has great potential, especially for high-dimensionality data and small training sets (Lizarazo 2008;Melgani and Bruzzone 2004). This feature is highly advantageous, especially for OOC, where object samples tend to be fewer than in pixel-based approaches.…”

Section: Image Classification and Assessmentmentioning

confidence: 98%

Texture extraction for object-oriented classification of high spatial resolution remotely sensed images using a semivariogram

Yue

Zhang

Yang

et al. 2013

International Journal of Remote Sensing

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2013)Texture extraction for object-oriented classification of high spatial resolution remotely sensed images using a semivariogramA Semivariogram, as defined in geostatistics, is a powerful tool for texture extraction of remotely sensed images. However, the traditional texture features extracted by a semivariogram are generally for pixel-based classification. Moreover, most studies have been based on the original computation mode of semivariogram and discrete semivariance values. This article describes a set of semivariogram texture features (STFs) based on the mean square root pair difference (SRPD) to improve the accuracy of object-oriented classification (OOC) in QuickBird images. The adaptive parameters for the calculation of a semivariogram were first derived from semivariance analysis, including directions, moving window size, and lag distance. Then, 22 STFs were extracted from the discrete and mean/standard deviation semivariance, and 15 features were selected from the extracted STFs based on feature optimization. Then five grey-level co-occurrence matrix (GLCM) texture features (mean, homogeneity, contrast, angular second moment, and entropy) were calculated based on segmented image objects using the panchromatic band. A comparison of classification results demonstrates that the STFs described in this article are useful supplement information for the spectral OOC, and the spectral + STFs classification method can be used to obtain a higher classification accuracy than can the combination of spectral and GLCM features.

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Section: Image Classification and Assessmentmentioning

confidence: 98%

Texture extraction for object-oriented classification of high spatial resolution remotely sensed images using a semivariogram

Yue

Zhang

Yang

et al. 2013

International Journal of Remote Sensing

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“…Also for airborne hyperspectral data, the number of studies using such techniques is increasing. An interesting example is the use of SVM to model and classify a segmented image [133,147].…”

Section: Hyperspectral Approachesmentioning

confidence: 99%

Can the Future EnMAP Mission Contribute to Urban Applications? A Literature Survey

et al. 2011

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With urban populations and their footprints growing globally, the need to assess the dynamics of the urban environment increases. Remote sensing is one approach that can analyze these developments quantitatively with respect to spatially and temporally large scale changes. With the 2015 launch of the spaceborne EnMAP mission, a new hyperspectral sensor with high signal-to-noise ratio at medium spatial resolution, and a 21 day global revisit capability will become available. This paper presents the results of a literature survey on existing applications and image analysis techniques in the context of urban remote sensing in order to identify and outline potential contributions of the future EnMAP mission. Regarding urban applications, four frequently addressed topics have been identified: urban development and planning, urban growth assessment, risk and vulnerability assessment and urban climate. The requirements of four application fields and associated image processing techniques used to retrieve desired parameters and create geo-information products have been reviewed. As a result, we identified promising research directions enabling the use of EnMAP for urban studies. First and foremost, research is required to analyze the spectral information content of an EnMAP pixel used to support material-based land cover mapping approaches. This information can subsequently be used to improve urban indicators, such as imperviousness. Second, we identified the global monitoring of urban areas as a promising field of investigation taking advantage of EnMAP's spatial coverage and revisit capability.Remote Sens. 2011, 3 1818However, owing to the limitations of EnMAPs spatial resolution for urban applications, research should also focus on hyperspectral resolution enhancement to enable retrieving material information on sub-pixel level.

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“…Tison et al (2004) proposed an algorithm based on the Markov Random Field (MRF) model and the fisher distribution. Lizarazo (2008) presented a classification method of remotely sensed data based on support vector machines (SVM). Lizarazo (2008) suggested that SVM may achieve acceptable classification accuracy at low computational cost.…”

Section: Introductionmentioning

confidence: 99%

“…Lizarazo (2008) presented a classification method of remotely sensed data based on support vector machines (SVM). Lizarazo (2008) suggested that SVM may achieve acceptable classification accuracy at low computational cost. SVM has become a popular alternative to traditional image classification methods because it makes possible accurate classification from a small set of training samples.…”

Section: Introductionmentioning

confidence: 99%

SAR image classification based on alpha-stable distribution

Yan

Zhou

et al. 2011

Remote Sensing Letters

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We propose a classification algorithm that utilizes the alpha-stable distribution to model the texture features of synthetic aperture radar (SAR) images. The SAR image is first decomposed by stationary wavelet transform (SWT). After that, the alpha-stable distribution is applied to model the high-frequency subband coefficients of the image at each decomposition scale. A regression-type method is then used to estimate the alpha-stable distribution parameters, which form a feature vector that fully describes the texture. Finally, a SAR image classification algorithm is derived by exploiting this feature vector based on the support vector machines (SVM) approach. Because different combinations of alpha-stable distribution parameters contribute to differences in classification precision, a multilevel SVM (MSVM) classification algorithm is also presented to address the issue. Experimental results indicate that the proposed SAR image classification algorithm is effective and the MSVM algorithm improves the classification performance. Moreover, our proposed algorithm has low computational cost as only a small number of the alpha-stable distribution parameters are processed.

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SVM‐based segmentation and classification of remotely sensed data

Cited by 42 publications

References 10 publications

Texture extraction for object-oriented classification of high spatial resolution remotely sensed images using a semivariogram

Texture extraction for object-oriented classification of high spatial resolution remotely sensed images using a semivariogram

Can the Future EnMAP Mission Contribute to Urban Applications? A Literature Survey

SAR image classification based on alpha-stable distribution

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